Digital images and videos carry noises resulting from a variety of noise sources such as image and video capture hardware and post-capture image processing techniques. Gaussian noise, which is a type of noise with a Gaussian amplitude distribution, is one of the major noise components in current digital images and image processing techniques. Specifically, an amount of noise is added to every part of the image. Each pixel in the image is changed from its original value by a small amount. Taking a plot of the amount of distortion of a pixel against the frequency with which it occurs produces a Gaussian distribution of noise.
Image de-noising is a technique to reduce or eliminate noises in images for many purposes, such as aesthetic visual experience and practical applications (i.e. computer imaging). Conventional de-noising techniques, such as spatial noise reduction techniques and temporal noise reduction techniques, employ edge-adaptive and motion adaptive algorithms to maintain sharpness of images. However, these existing techniques, including techniques for reducing including Gaussian noise, either fail to maintain high frequency details in textured detailed structures or are costly, in addition to possibly introducing artificial effect such as unwanted motion blurring.